Image databases have a variety of uses. In one important use, systems that identify objects of a particular generic type may acquire recognition capability through “training” based on a database of photographs of such objects; by “training” is meant creation and refinement of a system, such as a series of probes, that facilitate search and matching. Alternatively, the database may be used directly as a reference set for comparison without training. In either case, the photographs are analyzed to yield empirically-determined properties which serve as probes for the objects to be identified. For any particular identification to be performed, the efficacy depends, in part, on how well the image database actually spans the type of object and the conditions under which the photographs were taken. For example, when trying to identify a target human face, the reliability of the identification depends on whether the database includes photographs of individuals who resemble the target taken with pose and lighting conditions that correspond to that of the target face.
A key limitation of conventional identification training systems is that they rely on existing photographs. In situations where the existing photographs include individuals taken with poses and lighting which correspond, even approximately, to those of the target, the training database may yield effective probes. However, when the database photographs do not include such images, the database will be less useful, since it will not contain images that resemble the target. Conventional training systems are usually unable to remedy this problem without appropriate photographic data corresponding to the target. Often, photographs taken from a standard pose are available, but the target view is from a non-standard pose. Using the example of a human face again, if the database contains mainly front-view photographs, a captured front view of the target will be identifiable, but a side view may not be. If a database of side view photographs is not available, the system may fail to identify the target.
Since conventional training systems rely on existing sources of imagery, they may be forced to combine images of many individual examples of the class of object to be identified in order to obtain a large enough database from which to draw statistically reliable probes. This has the effect of making the probes more generic, since they now detect features common to a set of objects rather than to an individual object. This can present a problem when identification of a particular individual object is desired.
Accordingly, there exists a need for a practical approach that creates an image database that can be used effectively for identification purposes when available photographic data is inadequate.